From a review of the nation's metropolitan areas, David Rusk, a former mayor of Albuquerque,
develops the hypothesis that metropolitan areas in which central cities have been able to expand
(annex) have experienced more favorable social and economic results than those in which
annexation is limited. His book, Cities without Suburbs(1) concludes from this analysis that local
government consolidation (metropolitan government) and regional tax base sharing should be
encouraged and that regulations should be implemented to severely limit suburbanization. Rusk
has been retained to apply his principles to individual metropolitan areas around the nation, and
his work has given new life to efforts to regionalize governments and tax bases.

The City Elasticity Hypothesis

Rusk's conclusion that metropolitan health is driven by central city elasticity rests largely on an
analysis 117 metropolitan areas containing central cities with populations of more than 100,000
in 1990. He classifies these areas into five categories (quintiles) of "relative elasticity," based
upon the percentage increase in central city land area expansion from 1950 to 1990.

Rusk generally concludes that:

Metropolitan areas with more "elastic" central cities have higher rates of job creation than
metropolitan areas with less elastic central cities.

Metropolitan areas with more "elastic" central cities have higher average incomes than
metropolitan areas with less elastic central cities.

Metropolitan areas with more "elastic" central cities have higher population growth than
metropolitan areas with less elastic central cities.

Metropolitan areas with more "elastic" central cities have smaller central city-suburb
income gaps and smaller concentrations of poverty than metropolitan areas with less
elastic central cities.

Metropolitan areas with more "elastic" central cities have less residential racial
segregation than metropolitan areas with less elastic central cities.

According to Myron Orfield,

...David Rusk showed that areas that had created metropolitan governments by
consolidation or annexation were less segregated by race and class, more fiscally sound,
and economically healthier.(2)

Critique

The core areas of American (not to mention European) metropolitan areas have been losing
population for decades, as people have moved first to the outer reaches of the central cities and
more recently, to the suburbs beyond the borders of the central cities. There are a number of
reasons for this, both market and non-market. Examples of market factors are falling household
sizes, which has increased the demand for housing increased affluence and the democratization
of automobility. Examples of non-market factors are higher central city crime rates, substandard
education and higher central city taxes. As a result, virtually all central cities that have not
expanded their boundaries have lost population (except where their boundaries included large
tracts of undeveloped land). Even within the central cities that have expanded their boundaries,
population losses have been sustained in their cores. Rusk thus correctly observes that central
cities can generally increase their population only if they annex, because population density
(population per square mile) is generally falling. However, the associations between central city
elasticity and favorable metropolitan performance that Rusk asserts are largely happenstance,
arising from wholly unrelated factors.

All but one of the 23 "zero elasticity" (Quintile 1) metropolitan areas is in the older
industrial areas of the East and Midwest. The two lowest elasticity quintiles (1 and 2)
contain 33 metropolitan areas from the East and Midwest and 12 from the South and
West.

All 25 of the hyper elasticity (Quintile 5) metropolitan areas is in the South and West.
The two highest elasticity quintiles (4 and 5) contain 43 metropolitan areas from the
South and West and five from the East and Midwest.

Table #1
Rusk City Elasticity Sample by Region

Rust
Belt/Sun
Belt

BEA Region

Elasticity Quintile

Zero
Quintile 1

Low
Quintile 2

Medium
Quintile 3

High
Quintile 4

Hyper
Quintile 5

East &
Midwest
(Rust
Belt)

Northeast

5

2

0

0

0

Mid East

12

3

0

0

0

Great Lakes

3

6

8

3

0

Plains

2

0

3

2

0

South &
West
(Sun
Belt)

Southeast

0

5

7

7

11

Southwest

0

0

1

5

7

Rocky Mountains

0

0

1

0

1

Far West

1

6

4

6

6

EAST & MIDWEST

22

11

11

5

0

SOUTH & WEST

1

11

13

18

25

TOTAL

23

22

24

23

25

% IN SOUTH & WEST

4.3%

50.0%

54.2%

78.3%

100.0%

Since World War II, there has been a substantial transfer of population and economic activity
from the East and Midwest to the South and West.

From 1950 to 1990, 72 percent of population growth was in the Sun Belt.

From 1950 to 1990, 58 percent of economic growth was in the Sun Belt, as the area
increased from 37 percent to 53 percent of economic activity.

It is therefore to be expected that any classification of metropolitan areas that reflects a strong
Rust Belt versus Sun Belt composition will also reflect the regional demographic and economic
dynamics.

A regional analysis of average income growth yields results similar to Rusk's. When
regional income growth is substituted for the individual metropolitan area data, the zero
elasticity (Quintile 1) metropolitan areas score 4.4 percent below the average employment
growth, compared to Rusk's minus 2.5 percent. The hyper elasticity (Quintile 5)
metropolitan areas score 13.9 percent above the average in the regional analysis,
compared to 12.6 percent in the Rusk analysis. And again, the similarity carries through
to the Rust Belt-Sun Belt indicators. (Table #3).(4)

Table #3
Change in Per Family/Household Income: Variation from Mean

Quintile

Elasticity

Quintile

Rusk
1949-89

Substitute
Regional
Values
1949-89

Substitute
Rust Belt/
Sun Belt
Values
1949-89

1

Zero Elasticity

-2.5%

-4.4%

-7.4%

2

Low Elasticity

-14.4%

-3.4%

-2.7

3

Medium Elasticity

0.6%

-2.5%

-0.2%

4

High Elasticity

6.2%

0.9%

3.9%

5

Hyper Elasticity

10.1%

9.5%

6.4%

Range

12.6%

13.9%

13.8%

Calculated from Rusk & Census Bureau data.

The metropolitan areas in Rusk's classification represented 56 percent of the nation's population
in 1990. Yet substitution of regional data for specific metropolitan data yields similar results for
100 percent of the population.

Factors other than annexation policy have been more important in propelling the post-war job
and economic growth gap between the Rust Belt and the Sun Belt, such as:

The nation has become more homogeneous, as the interstate highway system, jet airline
service, telecommunications and other factors have made previously remote areas more
competitive.

Air conditioning has made the hot and humid summers in the South more bearable. As a
result, the South has competed more successfully as a region in which to work, live and
retire.

Taxes are generally lower in the Sun Belt. In 1996, state and local taxes per capita were
26 percent higher in the Rust Belt. The regional bias of the Rusk quintiles is evident in
the tax data. Application of the regional state and local tax rates to Rusk's quintiles
demonstrates that the heavily Rust Belt lower elasticity categories (Quintiles 1 and 2)
have significantly higher taxation than the higher elasticity categories (Quintiles 4 and 5).
The difference is even greater with respect to non-consumption taxes, especially
individual and corporate income taxes. High income taxes particularly discourage
business relocation, and have made the South and West generally more attractive than the
East and Midwest.

More businesses have been attracted to and established in the Sun Belt, where there is
room to build more spacious modern plants, and where labor costs are lower. For
example, only seven percent of Rust Belt employment was in right-to-work (voluntary
unionism) states, compared to 59 percent in Sun Belt states (James Bennett has found
disposable income and employment growth to be greater in right to work states).(5)

In addition to losing firms to the Sun Belt, the industrial base of Rust Belt metropolitan
areas has been challenged, as older, "smokestack" industries have fallen into decline due
to international competition and high labor costs.

Table #4
State and Local Taxation per Capita by Region: 1996

Quintile

Elasticity
Classification

All Taxes

Non-Consumption Taxes

Per Capita

Variation from
Mean

Per Capita

Variation from
Mean

1

Zero Elasticity

$3,100

19.4%

$2,029

38.6%

2

Low Elasticity

$2,680

3.2%

$1,567

7.0%

3

Medium Elasticity

$2,444

-5.9%

$1,344

-8.2%

4

High Elasticity

$2,390

-8.0%

$1,203

-17.8%

5

Hyper Elasticity

$2,287

-12.0%

$1,070

-26.9%

Range

&nbsp

31.3%

&nbsp

65.5%

There are other factors as well. For example, Katharine L. Bradbury, Anthony Downs and
Kenneth A. Small concluded in a 1980s study that factors such as climate (severity of winter)
and the presence of a state capital helped to determine metropolitan growth rates.(6)

With respect to economic growth, the central city elasticity hypothesis is holds only if it can be
shown that the Rust Belt to Sun Belt economic and population migration occurred as a result of
annexation policy. It must also be plausible that had the situation had been reversed, with high
central city elasticity in the East and Midwest and low in the South and West, that the growth
that has occurred over the past half century in the Sun Belt would have instead occurred in the
Rust Belt. This seems highly unlikely. The similarity between the central city elasticity results
and the regional and Rust Belt/Sun Belt analysis suggests that Rusk's categories simply reflect
underlying regional differences with respect to employment and income growth.

2. The Quintiles Reflect a Strong Metropolitan Size Bias: In addition to a regional imbalance,
the Rusk sample reflects a size imbalance. Only 46 of Rusk's 117 metropolitan areas had
populations of above 500,000 in 1950. Metropolitan areas in the zero and low elasticity quintiles
were overwhelmingly above 500,000 population in Rusk's base year of 1950 (Table #5). All 17
zero-elasticity (Quintile 1) metropolitan areas were above 500,000 population, while 15 of 29
high elasticity (Quintile 2) areas had populations of more than 500,000. At the same time, a large
percentage of metropolitan areas in the high and hyper elasticity quintiles were under 500,000 in
1950. Out of 38 hyper elasticity (Quintile 5) areas, 24 were below 500,000 in 1950, while 14 of
33 high elasticity (Quintile 4) areas were below 500,000. Since 1950, population growth (and job
growth) have been much higher in the smaller metropolitan areas, with metropolitan areas with
high and hyper elasticity central cities growing between 1.5 and six times as fast as metropolitan
areas with zero or low elasticity central cities. Smaller metropolitan areas have grown faster than
larger ones, and as a result have exhibited greater employment growth. And, as was shown
above, the greater economic growth has produced lower measures of residential segregation.
Rusk's results reflect much more about the characteristics of metropolitan size and growth than
annexation policy.

Table #5
1950 Metropolitan Population

Quintile

Elasticity Quintile

Metropolitan Population

>1,000,000

500,000-999,999

250,000-499,999

<250,000

1

Zero Elasticity

14

8

1

0

2

Low Elasticity

3

7

10

2

3

Medium Elasticity

0

4

8

12

4

High Elasticity

0

8

5

10

5

Hyper Elasticity

0

2

9

14

Total

17

29

33

38

1950-90 Average
Growth

48.6%

94.2%

143.2%

305.4%

From Rusk & calculated from Census Bureau data.

3. The Quintile Segregation Results Reflect Variances in Housing Turnover: Using the
Census Bureau's Black segregation index (based upon census tracts), Rusk finds that
metropolitan areas with elastic central cities tend to be less segregated. But again, there is a more
plausible explanation. It was not until the late 1960s, that legal racial barriers to housing were
eliminated in the United States. In 1990, many people continued to live in the same residences
that they had lived in when the 1970 census was conducted. Generally, metropolitan areas in the
Rust Belt had larger percentages of longer term residents than in Sun Belt metropolitan areas. In
fact, a regression analysis found a strong correlation between the 1990 percentage of people
living in their 1970 residences and the 1990 Census Bureau segregation index.(7)

Similar results
are found when comparing the segregation indexes of the elasticity quintiles with the
corresponding concentration of people still living in their 1970 residences (Table #6). In addition
to the statistical results, the residential tenure-segregation index relationship seems plausible,
because residential integration is likely to proceed at a greater rate where there is greater
economic growth, because there is greater turnover in the housing stock. This has occurred in the
newer, faster growing Sun Belt metropolitan areas as opposed to the older, slower growing Rust
Belt metropolitan areas. In residential segregation The cause of Rusk's segregation results does
not appear to be annexation practice, it is rather differences in housing turnover that are a
function of economic growth.

Table #6
Segregation and Housing Tenure

Quintile

Elasticity
Classification

Black
Segregation
Index

Percentage of
People Sill
Occupying 1970
Residence

1

Zero Elasticity

0.653

21.5%

2

Low Elasticity

0.575

19.6%

3

Medium Elasticity

0.579

17.9%

4

High Elasticity

0.514

15.0%

5

Hyper Elasticity

0.457

12.7%

Low Value Compared to High

69.9%

59.1%

Calculated from US Census Bureau data.

4. The Rusk Poverty and Segregation Results Derive from Data Masking: Broadly published
census reports do not provide data for components of municipalities. Where cities are smaller
(generally inelastic), much more localized data is readily available than where cities are larger
(generally elastic). As a result, in elastic cities there is the greater potential for masking the social
and economic characteristics that are typical of core areas.

Poverty: Rusk finds lower income disparity in metropolitan areas with greater central city
elasticity, noting that the gap between average income in the central cities and suburbs is less in
metropolitan areas with more elastic central cities.

But a closer examination of the data indicates the income disparity data is simply masked in the
elastic cities. As was noted above, the core areas of virtually all major cities have declined in
population over the past half century. As more Americans have achieved sufficient affluence to
buy their own homes, whether in the suburbs or the identical suburban-looking neighborhoods in
elastic central cities, the core neighborhoods they left behind were populated by lower income
people. This has occurred in both elastic and inelastic central cities. To test the income disparity
thesis, two Rusk "peer group" pairs were analyzed in detail: Elastic Nashville, which Rusk
compares with inelastic Louisville, and elastic Indianapolis, which Rusk compared to inelastic
Milwaukee.

In 1989, per capita income in the city of Indianapolis was 10 percent less than in its
suburbs, while per capita income in the city of Milwaukee was 38 percent below that of
its suburbs. But the Indianapolis data masks the fact that, within the 1950 boundaries of
the city, income disparity in 1989 was much greater --- 42 percent below that of the
central city and suburbs outside the 1950 boundaries. Indeed, the income disparity
between the 1950 core and the subsequently annexed portions of the city was a nearly
equal 41 percent.

In 1989, per capita income in the city of Nashville was two percent less than in its
suburbs, while per capita income in the city of Louisville was 22 percent below that of its
suburbs. But the Nashville data masks the fact that, within the 1950 boundaries of the
city, income disparity in 1989 was much greater --- 29 percent below that of the central
city and suburbs outside the 1950 boundaries. As in the case of Indianapolis, average
income within the 1950 city boundaries is well below that of annexed portions of the city,
at minus 26 percent.

Masking and Residential Segregation: Further, Rusk's observation that elastic metropolitan
areas have less segregated central cities is a function of masking within the larger elastic cities.

In 1990, the city of Indianapolis had a 22 percent Black population, compared to
Milwaukee's 30 percent. Yet, within its 1950 boundaries, 41 percent of the Indianapolis
population was Black.

In 1990, the city of Nashville had a 24 percent Black population, compared to
Louisville's 30 percent. Yet, within its 1950 boundaries, 54 percent of the Nashville
population was Black.

This masking of core area income and ethnic data is typical of US central cities. Both
Indianapolis and Nashville have annexed significantly through city-county consolidations and
experienced strong population gains from 1950 to 1990. But over the same period, each has lost
approximately 40 percent of the population that lived within the 1950 boundaries --- a loss of a
magnitude similar to that of well known population losers Detroit and Cleveland (both
approximately 45 percent). A similar dynamic has occurred in other US central cities --- whether
elastic or inelastic, population and income in the core has been dropping. The reality is the same,
even though the nature of census data publication makes it is less apparent to demographers.

Central City Data: Finally, Rusk's conclusions that poverty and racial segregation are greater in
inelastic central cities than in elastic central cities is just another manifestation of data masking.
Poverty appears to be greater in inelastic central cities because they have fewer more affluent
households to skew average income higher. The concentration of minority population is greater,
because fewer non-minority households are included in the average. But the situation are
virtually the same --- core area poverty and segregation tends to be greater in both elastic and
inelastic central cities

5. The Quintiles include Subcomponents of Metropolitan Areas: Early on, Rusk advises that
"the real city is the total metropolitan area --- city and suburb." Yet Rusk proceeds to base his
analysis on metropolitan subareas, not entire metropolitan areas. The Census Bureau classifies
metropolitan areas as "metropolitan areas" and "consolidated metropolitan areas," (CMAs) the
latter of which is comprised of two or more "primary metropolitan areas." Rusk's analysis is
based upon metropolitan areas and primary metropolitan areas (PMA), not CMAs. As a result
Rusk separates the Jersey City area --- barely a mile from Manhattan --- from the New York
metropolitan area, Orange County, California is considered separate from Los Angeles and the
East Bay suburbs are separate from metropolitan San Francisco. In fact, four of the component
parts of the New York consolidated metropolitan area are included among the 23 areas with zero
elasticity central cities. This failure to analyze the entire metropolitan area (CMA) skews the data
against the larger and older metropolitan areas, because their suburban PMAs tend to have more
favorable economic and social indicators, not least because they are newer.

6. Rusk's "New Home Buyers" Characterization is Flawed: Rusk calculates the number of
"new home buyers" from 1950 to 1990 (a more accurate term would be "new dwelling
occupiers"). For metropolitan areas with central cities that have grown, Rusk defines the number
of new home buyers as the net increase in population. But for metropolitan areas in which the
central city has lost population, Rusk defines the number of home buyers as the gain in suburban
(non-central city) population plus the number of people that have left the central city. Rusk
indicates that "For an inelastic area, new homes must be provided for newcomers to the metro
area and for current residents moving to the city from the suburbs." Rusk's "new home buyers"
indicator is simplistic and misleading. It assumes that residents in elastic central cities do not
move. This is, of course, absurd. Census data indicates that 63 percent of US households moved
between 1980 and 1990. For example, in the hyper-elastic central city of Phoenix, approximately
225,000 households occupied new dwelling units between 1980 and 1990, while the number of
households increased by only 100,000. Presumably someone had to move out of the 125,000
dwellings that were occupied by Phoenix "new home buyers" who lived in Phoenix in 1980.

7. Small Elastic Cities Could Not be So Influential. If it were true that city elasticity produced
superior economic and social results, then it would seem reasonable to believe that the best
performance would be achieved in metropolitan areas where the central cities accounted for a
higher percentage of the population. Yet six of the top eleven metropolitan areas in job creation
from 1973 to 1988 had central cities with 25 percent or less of their metropolitan population
(Orlando, Dallas-Fort Worth, Tampa-St. Petersburg, Raleigh-Durham, Sacramento and Atlanta).
It frankly seems implausible that central cities of such small comparative size could have such
significant impacts on their much larger suburbs and metropolitan areas,

8. Business Relocation Decisions do not Generally Consider Annexation Policy. Another
flaw in the central city elasticity hypothesis is that annexation policy simply does not emerge in
the literature as a factor of significance considered by corporations that are relocating or
establishing new facilities. It seems, for example, unlikely that the Walt Disney company
concerned itself with annexation policy when it made its 1960s decision to locate Disney World,
and set off an unprecedented rate of urbanization that made Orlando one of the top markets in
employment growth. It seems even more implausible that Orlando's superior economic and
social performance arises from the annexation policies of its hyper elastic central city, which
today contains barely 15 percent of the metropolitan population.

9. There is a Lack of Statistical Rigor: There is also a lack of analytical rigor in Rusk's survey.
No attempt is made to examine the rate of growth of metropolitan areas with elastic central cities
before and after major annexations, despite the fact that the annexations studied occurred over
the entire 40 year period. No analysis is provided that would eliminate alternative causes of the
phenomenon Rusk identifies, such as the regional and metropolitan size sample biases identified
above. At one point, Rusk indicates that the elasticity quintiles have "about the same number of
new-home buyers" yet the data described in the subject table (page 57) exhibits a nearly 70
percent range (from 484,703 to 818,853), well beyond the limits of similarity.

Conclusion

Accepting the Rusk thesis requires a childlike faith that the Rust Belt to Sun Belt migration of
the past half century was driven by municipal annexation policy. In fact, other factors, such as
economic growth, business cost differentials, weather and air conditioning, have driven the
changing economic and social fortunes of US metropolitan areas. That the comparatively
prospering metropolitan areas happen to be located in states with more liberal annexation laws
has no more driven their success than that their state speed limits have been generally higher.
Overall, southern and western metropolitan areas have done better than eastern and Midwestern
areas. Rusk's policy prescriptions of government consolidation, regional tax base sharing and
anti-suburban policies are inappropriate, not least because his diagnosis is flawed.

There is no dispute with Rusk's contention that the city is the entire metropolitan area, though he
himself fails to apply this principle. But the geographical boundaries of urban development are
not the optimal size for local governing units or local taxing districts. In his The Wealth and
Poverty of Nations, David Landes points out that innovation during the industrial revolution
occurred outside the reach of the existing municipalities, whose guilds would have stifled it ---
and did where annexation was permitted. It is a lesson we should not soon forget.

3. Rusk uses change in employment from 1973 to 1988. This analysis uses change in
employment from 1970 to 1990. If the central city elasticity hypothesis is valid,
the use of different years should produce similar results.

4. Rusk uses average family income from 1949 to 1989. This analysis uses a similar
measure, average household income from 1949 to 1989.

5. James T. Bennett, A Higher Standard of Living in Right to Work States,
(Springfield, VA: National Institute for Labor Research), 1990.

7. The regression produced an r-squared of 0.267, which is at the 99 percent level of
statistical significance for a sample of 94 metropolitan areas (primary metropolitan
statistical areas were included in their corresponding metropolitan statistical areas). The
resulting factors were: Constant: +0.364 and variable +0.416. The independent variable
was the percentage of people living in their residence in 1970 or before and the
dependent variable was the Census Bureau Black segregation index.